Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics

Abstract

Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology research to deal with large and complex imaging data sets. Nowadays, ML tools have become easily accessible to anyone. Such a low threshold to accessibility might lead to inappropriate usage and misinterpretation, without a clear intention. Therefore, ensuring methodological rigor is of paramount importance. Getting closer to the real-world clinical implementation of AI, a basic understanding of the main concepts should be a must for every radiology professional. In this respect, simplified explanations of the key concepts along with pitfalls and recommendations would be helpful for general radiology community to develop and improve their AI mindset. In this work, 22 key issues are reviewed within 3 categories: pre-modeling, modeling, and post-modeling. Firstly, the concept is shortly defined for each issue. Then, related common pitfalls and best practices are provided. Specifically, the issues included in this article are validity of the scientific question, unrepresentative samples, sample size, missing data, quality of reference standard, batch effect, reliability of features, feature scaling, multi-collinearity, class imbalance, data and target leakage, high-dimensional data, optimization, overfitting, generalization, performance metrics, clinical utility, comparison with conventional statistical and clinical methods, interpretability and explainability, randomness, transparent reporting, and sharing data. M edical images are complex and include huge amounts of minable data. adiomics simply aims to extract high-dimensional data from clinical images, to find clinically meaningful correlations and models. 2,3 However, complexity and high dimensionality introduced by radiomics exceed not only human comprehension but also the capabilities of traditional statistical tools. Artificial intelligence (AI) is now regarded as one of the attractive ways to analyze and make predictions on large and heterogeneous data sets as commonly seen with radiomic approaches.

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